library(dplyr)
library(ggplot2)
library(plotly)
library(gapminder)
library(tidyr)
library(countrycode)
library(forcats)
library(tidyverse)
library(gifski)
library(png)
library(gganimate)
library(ggthemes)
library(hrbrthemes)
library(GGally)
library(viridis)
world <- map_data("world") %>%
filter(region != "Antarctica") %>%
mutate(
continent = countrycode(sourcevar = region,
origin = "country.name",
destination = "continent"),
) %>%
drop_na(continent)
## Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Ascension Island, Azores, Barbuda, Bonaire, Canary Islands, Chagos Archipelago, Cocos Islands, French Southern and Antarctic Lands, Grenadines, Heard Island, Kosovo, Madeira Islands, Micronesia, Saba, Saint Martin, Siachen Glacier, Sint Eustatius, South Georgia, South Sandwich Islands, Virgin Islands
glimpse(world, width = 50)
## Rows: 93,989
## Columns: 7
## $ long <dbl> -69.89912, -69.89571, -69.9421~
## $ lat <dbl> 12.45200, 12.42300, 12.43853, ~
## $ group <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ order <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,~
## $ region <chr> "Aruba", "Aruba", "Aruba", "Ar~
## $ subregion <chr> NA, NA, NA, NA, NA, NA, NA, NA~
## $ continent <chr> "Americas", "Americas", "Ameri~
png("myplot.png")
continent_map <- ggplot(data = world) +
geom_map(map = world,
aes(long, lat, group = group, map_id = region,
fill = continent)) +
theme_map() +
coord_map(xlim = c(-180, 180),
ylim = c(-200, 200)) +
scale_fill_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
guides(fill = "none") +
theme(
plot.background = element_rect(color = "#B8C2CC", fill = NA)
)
## Warning: Ignoring unknown aesthetics: x, y
final_map <- ggplot(data = world) +
geom_map(map = world,
aes(long, lat, group = group, map_id = region,
fill = continent)) +
theme_map() +
scale_fill_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
guides(fill = FALSE) +
coord_map(xlim = c(-180, 180),
ylim = c(-200, 200))
## Warning: Ignoring unknown aesthetics: x, y
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
print(final_map)
dev.off()
## png
## 2
gapminder = read.csv('gapminder_full.csv')
p <- ggplot(gapminder, aes(gdp_cap, life_exp)) +
geom_point(aes(size = population, frame = year, ids = country, color = continent), alpha = 0.6) +
scale_x_log10() +
scale_color_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
scale_size_continuous(range = c(1, 15)) +
labs(title = 'Gapminder',
subtitle = 'Gapminder',
x = 'Income per capita (Dollars)',
y = 'Life Expectancy (Year)') +
theme_minimal()
fig <- ggplotly(p) %>%
animation_opts(
1000, easing = "elastic", redraw = FALSE
) %>%
animation_slider(
currentvalue = list(prefix = "YEAR ", font = list(color="red"))
) %>%
layout(
images = list(
source = base64enc::dataURI(file = "myplot.png"),
x = 0.7, y = -0.03,
sizex = 0.4, sizey = 0.4,
xref = "paper", yref = "paper",
xanchor = "left", yanchor = "bottom"
)) %>%
layout(title = list(text = paste0('Evolution of life Expentancy over the years',
'<br>',
'<sup>',
'Life expectancy, income per capita and population by country (and continent) evolution every 5 years since 1952 ',
'</sup>')))
fig
years_school = read.csv('mean-years-of-schooling-long-run.csv')
colnames(years_school) <- c('country','code','year', 'years_school_avg')
years_school$year[years_school$year == 1950] <- 1952
years_school$year[years_school$year == 1955] <- 1957
years_school$year[years_school$year == 1960] <- 1962
years_school$year[years_school$year == 1965] <- 1967
years_school$year[years_school$year == 1970] <- 1972
years_school$year[years_school$year == 1975] <- 1977
years_school$year[years_school$year == 1980] <- 1982
years_school$year[years_school$year == 1985] <- 1987
test <- merge(gapminder, years_school, by=c("country","year"), all.x= TRUE)
test$code <- NULL
test_2 <- na.omit(test) %>%
group_by(year, continent) %>%
summarise(a_mean=(mean(years_school_avg)))
## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
accumulate_by <- function(dat, var) {
var <- lazyeval::f_eval(var, dat)
lvls <- plotly:::getLevels(var)
dats <- lapply(seq_along(lvls), function(x) {
cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
})
dplyr::bind_rows(dats)
}
p <- ggplot(test, aes(years_school_avg, life_exp, color = continent)) +
geom_point(aes(frame = year, ids = country), alpha = 0.6, show.legend = FALSE) +
theme(legend.position="none") +
scale_color_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
scale_size_continuous(range = c(1, 15)) +
labs(title = '',
subtitle = '',
x = 'years_school_avg',
y = 'life_exp') +
theme_minimal()
fig1 <- ggplotly(p)
p2 <- ggplot(test, aes(years_school_avg, gdp_cap)) +
geom_point(aes(frame = year, ids = country, color = continent), alpha = 0.6, show.legend = FALSE) +
scale_y_log10() +
scale_color_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
scale_size_continuous(range = c(1, 15)) +
labs(title = '',
subtitle = '',
x = 'years_school_avg',
y = 'gdp_cap') +
theme_minimal()
fig2 <- ggplotly(p2 + guides(colour=FALSE))
df <- test_2
fig3 <- df %>% accumulate_by(~year)
fig3 <- fig3 %>%
plot_ly(
x = ~year,
y = ~a_mean,
split = ~continent,
frame = ~frame,
type = 'scatter',
mode = 'markers',
color = ~continent,
showlegend=FALSE,
colors = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF"),
line = list(shape = "linear"),
inherit = TRUE
)
fig3 <- fig3 %>% layout(
xaxis = list(
title=list(text="years", font=list(size=14)),
zeroline = T
),
yaxis = list(
title=list(text="years school avg", font=list(size=14))
)
)
fig <- subplot(fig1, fig2, fig3, nrows = 3, shareX = F, shareY = F, titleY = T, titleX = T,margin = 0.07) %>%
layout(title = list(text = paste0('Evolution of life expectancy and gdp per capita against years education by country',
'<br>',
'<sup>',
'Countries with more years of education on average have higher life expectancy and income',
'</sup>')))
fig <- fig %>% animation_opts(
frame = 1000,
transition = 0,
redraw = FALSE
)
fig <- fig %>% animation_slider(
hide = F
)
fig <- fig %>% animation_button(
currentvalue = list(prefix = "YEAR ", font = list(color="red"))
)
fig %>% style(fig, showlegend = FALSE) %>%
layout(
images = list(
source = base64enc::dataURI(file = "myplot.png"),
x = 0.75, y = 0.59,
sizex = 0.3, sizey = 0.3,
xref = "paper", yref = "paper",
xanchor = "left", yanchor = "bottom"
))
data <- read.csv('average-oecd-education-expenditure-by-source-of-funding-gdp.csv')
colnames(data) <- c("ent", "code", "year","private", "public" )
fig <- plot_ly(data, x = ~year, y = ~private, name = 'private expenditure', type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D') %>% layout(yaxis = list(ticksuffix = "%", range = c(0, 4)))
fig <- fig %>% add_trace(y = ~public, name = 'public expenditure', fillcolor = '#4C74C9')
fig <- fig %>% layout(title = 'Average OECD non-tertiary education expenditure by source of funding',
xaxis = list(title = "",
showgrid = FALSE),
yaxis = list(title = "Expenditures (in percent of PIB)",
showgrid = FALSE))
fig
data_2 = read.csv('gender-ratios-for-mean-years-of-schooling.csv')
accumulate_by <- function(dat, var) {
var <- lazyeval::f_eval(var, dat)
lvls <- plotly:::getLevels(var)
dats <- lapply(seq_along(lvls), function(x) {
cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
})
dplyr::bind_rows(dats)
}
df <- data_2
fig <- df %>% accumulate_by(~Year)
fig <- fig %>%
plot_ly(
x = ~Year,
y = ~Regional.female.to.male.years.schooling..Lee.Lee..2016..,
split = ~Entity,
frame = ~frame,
type = 'scatter',
mode = 'markers',
line = list(shape = "linear"),
inherit = TRUE
)
fig <- fig %>% layout(
xaxis = list(
title = "Year",
zeroline = F
),
yaxis = list(
title = "Female-to-male ratio of average years of schooling,",
zeroline = F
)
)
fig <- fig %>% animation_opts(
frame = 500,
transition = 0,
redraw = FALSE
)
fig <- fig %>% animation_slider(
hide = T
)
fig <- fig %>% animation_button(
x = 1, xanchor = "right", y = 0, yanchor = "bottom"
)
fig %>%
layout(title = list(text = paste0('Gender ratios for mean years of schooling, 1870 to 2010',
'<br>',
'<sup>',
'Female-to-male ratio of average years of schooling, expressed in percents. All education levels for population aged 15-64',
'</sup>')))
library(dplyr)
library(countrycode)
prueba = read.csv('correlation-between-child-mortality-and-mean-years-of-schooling-for-those-aged-15-and-older.csv')
prueba <- prueba[prueba$Year %in% c(1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010), ]
prueba <- na.omit(prueba)
prueba$Continent <- countrycode(sourcevar = prueba[, "Entity"],
origin = "country.name",
destination = "continent")
colnames(prueba) <- c('country', 'code', 'year', 'under_five_mortality', 'mean_years_schooling_women', 'population', 'continent')
p <- ggplot(prueba, aes(under_five_mortality, mean_years_schooling_women)) +
geom_point(aes(frame = year, ids = country, color = continent), alpha = 0.6) +
scale_x_log10() +
labs(title = 'Gapminder',
subtitle = 'Gapminder',
x = 'percent under five mortality',
y = 'mean years schooling women') +
theme_minimal()
fig <- ggplotly(p) %>%
animation_opts(
1000, easing = "elastic", redraw = FALSE
) %>%
animation_slider(
currentvalue = list(prefix = "YEAR ", font = list(color="red"))
) %>%
layout(title = list(text = paste0('Under five mortality against women education mean years',
'<br>',
'<sup>',
'Women’s education is inversely correlated with child mortality',
'</sup>')))
fig
prueba_2 <- prueba %>%
group_by(year, continent) %>%
summarise(a_mean=(mean(under_five_mortality)))
fig1 <- plot_ly(data = prueba_2, type = "bar",
x = ~continent, y = ~a_mean, frame = ~year, showlegend = FALSE)
fig1 <- fig1 %>% layout(
xaxis = list(
title = "Continent",
zeroline = F
),
yaxis = list(
title = "percent under five mirtality",
zeroline = F
)
)
df <- prueba_2
fig2 <- df %>% accumulate_by(~year)
fig2 <- fig2 %>%
plot_ly(
x = ~year,
y = ~a_mean,
split = ~continent,
frame = ~frame,
type = 'scatter',
mode = 'markers',
line = list(shape = "linear"),
inherit = TRUE
)
fig2 <- fig2 %>% layout(
xaxis = list(
title = "Year",
zeroline = F
),
yaxis = list(
title = "under_five_mortality",
zeroline = F
)
)
fig <- subplot(fig1, fig2, nrows = 2, shareX = F) %>%
layout(title = 'Evolution of under five mortality by continent')
fig <- fig %>% animation_opts(
frame = 1000,
transition = 0,
redraw = FALSE
)
fig
gob_exp = read.csv('liberal-democracy-today-vs-past-schooling.csv')
gob_exp <- na.omit(gob_exp)
gob_exp$continent <- countrycode(sourcevar = gob_exp[, "Entity"],
origin = "country.name",
destination = "continent")
gob_exp_2 <- gob_exp %>%
group_by(Entity, continent) %>%
summarise(years_schooling_past=(mean(Total.years.of.schooling..Lee.Lee..2016..)),
libdem_vdem_owid = last(libdem_vdem_owid, order_by = Year),
year = last(Year, order_by = Year),
population = (mean(Population..historical.estimates.)))
gob_exp_2 <- na.omit(gob_exp_2)
fig_1 <- gob_exp_2 %>%
ggplot(aes(x = years_schooling_past, y = libdem_vdem_owid, size = population, color = continent, ids = Entity)) +
scale_color_manual(values = c("#F15772", "#00FF00",
"#FBE700", "#000080", "#FF00FF")) +
geom_point(alpha = 0.6, ) +
labs(title = 'Liberal democracy today vs. past average years of schooling',
subtitle = 'Year 2021',
caption = 'Data from Kaggle',
x = 'Mean years of schooling in 1970',
y = 'Liberal democracy') +
theme_minimal()
ggplotly(fig_1) %>%
layout(
images = list(
source = base64enc::dataURI(file = "myplot.png"),
x = 0.8, y = 0.01,
sizex = 0.5, sizey = 0.5,
xref = "paper", yref = "paper",
xanchor = "left", yanchor = "bottom"
)) %>%
layout(title = list(text = paste0('Liberal democracy today vs. past average years of schooling',
'<br>',
'<sup>',
"impact of investment in education in the past",
'</sup>')))